Is There an Optimal Industry Capital Structure?

Is There an Optimal Industry Capital Structure?

                                    Peter MacKay and Gordon M. Phillips*

                                                     Abstract

We examine the effect of industry on a firm’s financial structure testing the simultaneous relationships
between financial leverage, technology, and risk. We document considerable intra-industry variation
in financial leverage within 44 competitively-structured U.S. manufacturing industries between 1977
and 1990. Simple analysis of variance results show that industry fixed effects are less important than
firm fixed effects in understanding the variation in firm financial structure. Despite this result that
most financial structure variation is firm specific, a large number of firms remain in the same financial
leverage industry-quintile over time. Our regression analysis shows that firms do take into account
other firms’ financial leverage within their industry. In addition, capital structure is significantly
related to the extent a firm departs from the industry median technology choice. Changes in financial
leverage are positively associated with changes in profitability within an industry. Our results provide
support for industry equilibrium models of capital structure in which capital structure, risk and
technology are simultaneous, endogenous decisions.

                                                                    Comments Welcome
                                                                    First Draft: February 15, 2001
                                                                    Current Version: May 14, 2001

*
 MacKay is from the Edwin L. Cox School of Business, Southern Methodist University and Phillips is from the Robert H.
Smith School of Business, University of Maryland. We thank Mike Long, Nagpurnanand Prabhala, Alexander Reisz and
seminar participants at the Atlanta Finance Consortium, Rutgers conference on capital structure, Southern Methodist
University and the University of Kentucky for helpful comments. MacKay can be reached by email at
pmackay@mail.cox.smu.edu, homepage: http://faculty.cox.smu.edu/pmackay.html. Phillips can be reached by email at
GPhillips@rhsmith.umd.edu, homepage: www.rhsmith.umd.edu/Finance/gphillips/. The research was conducted at the
Center for Economic Studies, U.S. Bureau of the Census, Department of Commerce. The authors alone are responsible for
the work and any errors or omissions.

Is There an Optimal Industry Capital Structure?

                                               Abstract

We examine the effect of industry on a firm’s financial structure testing the simultaneous relationships
between financial leverage, technology, and risk. We document considerable intra-industry variation
in financial leverage within 44 competitively-structured U.S. manufacturing industries between 1977
and 1990. Simple analysis of variance results show that industry fixed effects are less important than
firm fixed effects in understanding the variation in firm financial structure. Despite this result that
most financial structure variation is firm specific, a large number of firms remain in the same financial
leverage industry-quintile over time. Our regression analysis shows that firms do take into account
other firms’ financial leverage within their industry. In addition, capital structure is significantly
related to the extent a firm departs from the industry median technology choice. Changes in financial
leverage are positively associated with changes in profitability within an industry. Our results provide
support for industry equilibrium models of capital structure in which capital structure, risk and
technology are simultaneous, endogenous decisions.

                                                                                                       2

As Myers (1984) notes in his paper on the capital structure puzzle there are many things

that we still do not know about capital structure. Despite the extensive research surveyed by

Harris and Raviv (1991), and research on the cross-sectional determinants of capital structure by

Titman and Wessells (1988), Opler and Titman (1994), Rajan and Zingales (1995), important

puzzles still remain.1         Empirically, Bradley, Jarrell and Kim (1984) show that there is an

extensive amount of intra-industry variation after controlling for industries.2 We still do not

know empirically the significance of industry factors to a firm’s capital structure decision and

thus what may drive intra-industry variation in capital structure. Also unknown is the empirical

relevance of recent theoretical models (Dammon and Senbet, 1988, Leland, 1998) that stress the

simultaneity of real and financial decisions.

           In this paper we examine whether this intra-industry variation in capital structure reflects

industry factors and the extent to which real and financial decisions are jointly determined within

competitive industries. 3 We examine the empirical predictions of models that endogenize firms’

real and financial decisions and studies which model these joint decisions in a competitive-

industry equilibrium (Maksimovic and Zechner, 1991, Williams, 1995, and Fries et al., 1997).

Similar to Miller’s (1977) irrelevance result, the latter studies illustrate how conclusions reached

in a representative-firm partial-equilibrium framework are fundamentally altered, even reversed,

as the equilibrium setting is aggregated to the level of an industry or the economy.

1
    Other recent empirical papers on capital structure decisions include Graham (1996) and Parrino and Weisbach
    (1999). Graham focus on incremental financing decisions and specifically how marginal tax rates induce firms to
    issue debt. Parrino and Weisbach take a firm’s capital structure as given and focus on whether various capital
    structures may cause a firm to take riskier projects because of conflicts of interest between shareholders and
    bondholders. We treat the capital structure and risk decisions as simultaneous.
2
    An early study that documents the extensive amount of intra-industry variation in financial structure is Remmers,
    Stonehill, Wright, and Beekhussen (1974). Chaplinsky (1983) also shows that industry dummy variables explain
    only a small amount of the variation in firm capital structure.
3
    Chevalier (1995), Phillips (1995) and Kovenock and Phillips (1997) show that firms take into account other firms’
    financial and real decisions in imperfectly competitive industries.

We investigate the impact of simultaneity, both as a practical empirical matter and as a

test of recent theory, by estimating single- and simultaneous-equation regressions for financial

leverage, capital-intensity, and cash flow volatility. We test predictions of industry-equilibrium

models by including measures of a firm’s position within its industry in these regressions, such

as the similarity of its capital-intensity to the industry mean capital-intensity, the actions of firms

inside and outside the firm’s industry quintile, and its status as an entrant, incumbent, or exiting

firm.

        We first precede these specific tests of industry-equilibrium models with a broader

investigation of inter- and intra-industry variation in capital structure. We document that there

exists cross-sectional variation in financial leverage within the 44 competitively-structured U.S.

manufacturing industries we examine between 1977 and 1990. Our initial results using a simple

analysis of variance show that most of the variation in financial structure arises within industries

rather than between industries.      Two-, three-, and four-digit SIC industry effects combined

account for only 8% of variation in financial structure. The remaining variation is within-

industry, of which 64% is between-firm and 28% is within-firm.               Variation in cash flow

volatility follows a similar breakdown between and within industries. In contrast, industry effects

explain 47% of the variation in capital-labor ratios (35% by two-digit SIC). Thus, while we

concur with Bradley et al. (1984) that industry factors affect financial structure, we show that

most of the variation in financial structure arises within industries and is firm specific.

        Given these initial results that show the relative unimportance of industry fixed effects to

capital structure, we examine the importance of industries to the within-industry dispersion in

capital structure. We examine the joint distribution of financial leverage, capital-intensity, and

cash flow volatility within industries. We do so by forming industry-year specific quintiles for

each of these variables and reporting quintiles means for the other variables. This allows us to

                                                                                                     2

detect linear and nonlinear relations between these variables and test central predictions of the

industry-equilibrium models of Maksimovic and Zechner (1991), Williams (1995) and Fries et

al. (1997).

       We show that although most variation in capital structure is within-industry, there is

persistence in firms’ position within their industry even in competitively-structured industries.

We find that firm’s do take into account the other firms’ financial leverage within their industry.

We find that a firm’s financial leverage is significantly positively related to that of other firms

within the same financial-leverage quintile, but not to that of firms in other quintiles within that

industry. In addition, examining firms over time, we find that more than 35 percent of firms

remain in the same financial leverage industry-quintile going from the period 1977-1983 to

1984-1990. These results suggest financial leverage is related to other firms’ choices within an

industry – even in competitive industries.

       Our tests show more direct support for the industry equilibrium models of capital

structure. We find that firms that depart from the industry median technology use more financial

leverage and experience greater cash flow volatility. Our evidence also shows that changes in

financial leverage are positively associated with changes in profitability and risk within an

industry. This finding is particularly of note, relative to the previous cross-industry evidence

reported by Harris and Raviv (1991) that the consensus of previous cross-sectional studies is that

financial leverage decreases with risk and profitability. This previously reported cross-sectional

negative relationship between financial leverage and profitability is reported by Myers (1989) to

be the most telling evidence against the static trade-off theory of capital structure. Our evidence

shows that this relationship is positive when examining intra-industry changes in capital

structure and profitability controlling for the endogeneity of both these variables.

                                                                                                  3

To investigate the dynamic aspects of the industry equilibria developed by Williams

(1995) and Fries et al. (1997), we present transition frequencies for firms entering, staying, or

leaving their industries. Comparing industry quintiles for the first and second periods (1977-1983

and 1984-1990), we find that for all variables we consider (financial leverage, capital-intensity,

risk, profitability, asset size, and natural hedge), persistence rates for incumbent firms are above

what would be expected if incumbent firms were uniformly redistributed across quintiles in the

second period. This suggests that even in competitively-structured industries, firm characteristics

evolve slowly and that firms retain their industry rankings.

       Williams (1995) predicts that because of dissipative perks and differential access to

capital, the equilibrium structure of industries is characterized by a core of large, stable,

profitable, capital-intensive, financially leveraged firms and a competitive fringe of small, risky,

non-profitable, labor-intensive firms. Support for this prediction is mixed in that firms in the

bottom asset-size quintile of their industry are riskier and less profitable, but more capital-

intensive and indebted than firms in the top asset-size quintile.

       Firms that enter an industry by building plants or buying existing plants carry

significantly more debt (33% of total assets) than incumbents (22%) or firms that exit by retiring

old plants (24%). Consistent with Williams (1995), entrants and exiters operate at the fringe of

their industries, being smaller, riskier, less profitable, and more labor-intensive than core

incumbent firms. Firms that leave an industry by selling existing plants are small firms from the

lower financial leverage and upper profitability quintiles. This suggests that financially

unconstrained firms sell their assets when profits are high, not to alleviate financial distress. In

contrast, firms that leave by retiring old plants are small firms from the upper financial leverage

and lower profitability quintiles, suggesting they face both financial and economic distress.

                                                                                                  4

Consistent with Maksimovic and Zechner (1991), these firms belong to the upper risk and lower

natural-hedge quintiles.

           Overall, our tests provide support for industry equilibrium models of firm financial

structure. We find firms that significantly deviate from the median industry capital structure also

significantly deviate from the median industry capital intensity, measured by their capital-labor

ratio. We find that financial leverage is positively related to profitability and risk within an

industry. We also find mixed support for predictions of the industry equilibrium model of

Williams (1995). Although incumbent firms are more capital intensive with higher profitability

and lower risk than “fringe” firms that exit or enter the industry, the latter use more debt.

           We organize the remainder of this paper as follows. Section II reviews the capital-

structure literature and develops empirical hypotheses from the industry-equilibrium models.

Section III describes our data sources, sample selection, and the variables we use to test these

hypotheses. Section IV discusses our univariate and multivariate results. Section V concludes.

II. The Importance of Industries to Financial Structure

           Most financial structure research has focused on the cross-sectional firm specific

characteristics that are related to firm financial structure. The importance of industry has not

been typically examined directly. Several authors do include industry fixed effects in their

analysis. Bradley, Jarrell and Kim (1984) include industry dummy variables in their regressions

and find firm-specific factors are more significant than industry factors.4 Sharpe (1994) shows

that the employment of firms with high financial leverage does vary more significantly with the

4
    Bradley, Jarrell and Kim collapse each firm’s yearly financial structure from 1962-1981 into a single cross-section.
    After excluding regulated industries, firm volatility, non-debt tax shields and advertising and R&D expenses,
    explain 23.6 percent of the variation in capital structure. Industry dummy variables add only 10.1 percent
    additional explanatory power. Industry dummy variables without the other explanatory variables explain 25.6
    percent of the cross-sectional variation in firm capital structure.

                                                                                                                      5

business cycle. However, the exact way industries influence firm financial structure is unknown,

given that there exists substantial within-industry variation in firm financial structure.

       Before we conduct more focused tests of industry-equilibrium models, we conduct two

simpler broader types of tests. First, we document the intra-industry variation in capital structure

using an analysis of variance. Using a panel, we regress a firm’s capital structure on firm fixed

effects and on industry-level fixed effects at the 2, 3 and 4 digit SIC code level. This analysis

documents the relative importance of industries versus firm fixed effects. We do this analysis on

a panel and also, excluding firm-effects, investigate the importance of year effects.

       Second, we break industries into quintiles based on six central variables: leverage, risk,

profitability, the capital-labor ratio, the divergence of a firm’s technology from the industry

median technology and firm size. Four of these variables, risk, profitability, the capital-labor

ratio and firm size, have been shown by many studies, as reported in Harris and Raviv (1991), to

be related to a firm’s capital structure choice. The fifth variable, the divergence of a firm’s

technology from the industry median technology, which is labeled a firm’s “natural hedge”, is

the key variable in industry equilibrium models. It captures the riskiness of a firm’s technology

choice, emphasizing that this riskiness is dependent on the technology choice of the other firm’s

in its industry. We show how each of these variables varies with quintiles formed using the other

variables. This analysis gives a detailed picture of the potential ways these key variables may

vary, with each other and nonlinearly, within an industry.

       Following this broader analysis, we examine specific predictions of industry equilibrium

models. Maksimovic and Zechner (MZ) (1991), Williams (1995) and Fries et al (1997) show

that industries can play a subtle role in the determination of within-industry capital. Thus, in the

reminder of this section we describe the main industry equilibrium models that we test in this

paper. We present some of the central predictions of these models and also describe the

                                                                                                   6

econometric tests that must be used to test these models. Put simply, these models emphasize the

simultaneity of capital structure, risk, and technology choice, and the endogeneity of firm within

industries. Thus, more so than tests of partial-equilibrium theories, our tests must address the

simultaneity of these decision variables and the endogeneity of firm characteristics.

       We begin first with the model of MZ (1991). MZ show that firms can either choose a

technology with a known marginal cost, the “safe” technology, or choose a technology with an

uncertain cost. All capital is raised with debt financing and managers maximize the value of

equity. In MZ, initially the risky technology may have higher profits and also higher risk.

Without taxes, in a model that ignores the industry equilibrium, firms do not finance with debt

because managers would pick the risky project. With a tax shield from debt, firms prefer debt.

Firms that adopt this technology will also finance with more debt.

       As more firms adopt the risky technology the aggregate output and thereby the price of

output more closely tracks that technology’s marginal cost of production.          Thus the risky

technology may in the end provide a better natural hedge against the uncertain marginal cost of

production.     In this model risk is endogenous as what makes one technology riskier is the

fraction of firms that choose that particular technology. In industry equilibrium, capital structure

becomes irrelevant as the profitability and risk of a project is determined not by ex ante

characteristics but rather by the number of firms that choose that technology. The more firms

that choose a given technology, the less risky and less profitable it becomes. Firms choose a

specific technology until the NPVs are equal across the two types of technologies. Thus what is

important is the how much firms depart from the industry median technology.

       Several testable empirical predictions follow from the MZ model. First, as firms depart

from the industry median technology they will also depart from the industry median capital

structure.    Second, because their equilibrium is characterized by high-debt, high-risk and low-

                                                                                                  7

debt, low-risk configurations, we should observe a positive relation between financial leverage

and cash flow volatility. Third, because a firm’s realized risk and expected cash flow depends on

the technology decisions of all firms in the industry, we should observe an inverse relation

between cash flow volatility and a firm’s similarity to the median technology (which we term the

“natural hedge”), and between profitability and the industry median technology.           What is

important about these predictions that both risk and financial structure are chosen endogenously

and thus our empirical estimation must control for both the endogeneity and the simultaneity of

the capital structure, risk and technology decisions.

       Williams (1995) extends the MZ industry equilibrium by allowing for endogenous entry

and exit, and exogenous perks consumption and differential access to capital. Williams assumes

that firms produce a homogeneous good using either a high variable-cost, labor-intensive

technology requiring no capital outlay, or a low variable-cost, capital-intensive technology

requiring capital-market financing. Managers cannot credibly commit to forego their dissipative

perks, so capital is rationed even though the capital market is perfectly competitive. As a result,

the NPV of capital-intensive firms is positive, even as the cost of entry goes to zero. Capital

intensive firms become the incumbent “core” firms with higher profits, as that technology is less-

risky. Agency problems cause debt to be optimal for these incumbent firms in order to prevent

these firms from consuming higher perquisites out of the money raised. Even as the cost of entry

converges to zero, capital-intensive firms will earn positive profits as agency problems prevent

“fringe” labor-intensive firms from raising capital and entering the industry.

       Like MZ, Williams (1995) characterizes the industry equilibrium distribution of debt and

firm characteristics, and explains firm heterogeneity within industries. Williams predicts an

asymmetric equilibrium industry structure characterized by a core of large, stable, profitable,

                                                                                                  8

capital-intensive, financially-leveraged firms and a competitive fringe of small, risky, non-

profitable, labor-intensive firms.

       The predictions from this model that we will test are whether fringe firms (identified by

examining firms that enter and exit industries) have higher risk, lower profitability and whether

they will be less capital intensive than incumbent firms.

       We use several empirical strategies to test the Williams (1995) equilibrium industry

structure. First, we produce separate analyses of variance of industry and firm effects for

incumbents and firms that enter or exit their industries (Table I). Second, we report differences

between industry-year adjusted means for incumbents and firms that enter or exit their industries

(Table II). Third, we use transition frequencies to test Williams’ prediction that firms, which

enter or exit an industry belong to the tails (“fringe’) of the industry distribution for financial

leverage, capital-labor, risk, profitability, size, and natural hedge (Table VII). Finally, we include

dummies for entry and exit in our multivariate regressions (Table VIII).

       Fries et al. (1997) use a contingents-claims approach to analyze optimal capital structure

in a competitive-industry equilibrium that combines features of the previous models. Like

Williams (1995), they allow for endogenous firm entry and exit. Like Maksimovic and Zechner

(1991), they incorporate shareholder-bondholder conflicts and corporate debt tax-shields. Fries et

al. (1997) find that as a result of a trade-off between tax advantages and agency costs, a firm

optimally adjusts its financial leverage upward after it is set up. To test this prediction, we test

for differences in financial leverage in the year of entry and in years following entry. We also

test for differences in financial leverage in the year of exit and in years preceding exit.

       We capitalize on certain similarities between the industry-equilibrium models. For

instance, by focusing on capital-labor ratios as a measure of technology, we directly incorporate

a feature of the Williams (1995) model, where firms are either capital-intensive or labor-

                                                                                                    9

intensive – with labor-intensive firms being riskier. We also use capital-labor ratios to implement

the MZ (1991) natural hedge concept. Using a firm’s capital-labor choice to proxy for its

technology choice, in MZ (1991) firms that depart from the industry average capital labor ratio

will be riskier.

        Finally, these models emphasize the joint nature of firms’ debt, technology, and risk

decisions. We therefore estimate our multivariate regression models using simultaneous-equation

methods. We include regression equations for financial leverage, capital-intensity, and cash flow

volatility. We model interactions between these dependent variables by including each of the

remaining two variables as regressors for the dependent variables featured in each equation. We

estimate the system of equations using three-stage least squares to reflect the correlation of the

residuals.

        The explanatory variables include our proxies for a firm’s technology position within its

industry (its natural hedge), the actions of firms inside and outside the firm’s own industry-

quintile, and its status as an entrant, incumbent, or exiting firm. We control for profitability, firm

size, diversification, industry, year, and firm fixed effects. Following MacKay (1998), we also

control for the ability a firm has to adjust its production level and capital stocks (proxies for

volume flexibility and investment flexibility).

III. Data Sources, Sample Selection, and Variable Construction

A) Data Sources
        Our primary data sources are the Longitudinal Research Database (LRD) and the

Quarterly Financial Reports (QFR), both maintained by the Center for Economic Studies at the

Bureau of the Census (U.S. Department of Commerce). The intercensus data are from the

Annual Survey of Manufacturers (ASM), comprising all plants with more than 250 employees

and a randomly selected, five-year rotating subpanel of smaller plants.

                                                                                                   10

The LRD database contains plant-level data that enables us to test theories that relate

technology choice to a firm’s financial structure. Specifically, it contains detailed information

on flows and stocks of factor inputs and production outputs (e.g. fixed-capital stocks, cost of

materials, employment numbers and value of shipments). It also identifies exit and entry,

including closure and plant sales, allowing us to test the dynamic nature of industry equilibrium

models of Williams and Fries et. al. These types of data and specific identification of entry, exit

and sales are not found in COMPUSTAT. Finally, the LRD covers both public and private firms

and each plant is classified by four-digit SIC code. This latter feature enables us to construct

measures of industry technology choice at the industry level instead of relying on firm-level

measures across multiple industries.

           We match the LRD database (aggregated up to firm-level observations) to the Quarterly

Financial Reports to obtain information about a firm’s debt choice.5 The QFR contains quarterly

firm-level accounting data (balance sheet and income statement accounts) for all public and

private firms above a cutoff size (measured by nominal assets), and a randomly selected, eight-

quarter rotating subpanel of smaller firms, of which one eighth is replaced each quarter. No

census/intercensus year distinction is made. The certainty cutoff size was $10 million from 1977

(Q1) to 1978 (Q2), $25 million from 1978 (Q3) to 1988 (Q3), and $50 million from 1988 (Q4)

onward. A key procedure in creating the sample involved matching LRD and QFR data using a

firm identifier common to both datasets.

B) Industry Selection
           We use competitively structured industries to comply with the assumptions of the

industry equilibrium models of Maksimovic and Zechner (1991) and Williams (1995). We

5
    For multiple industry firms we use the firm’s primary industry as it industry classification. In robustness tests we
     exclude firms that are very diversified (herfindahl of industry shipments < .5) and find the same results with
     respect to sign and significance.

                                                                                                                     11

implement this criterion by retaining industries with no fewer than fifteen firms over the span of

the empirical period (1977-1990). This screen leaves us with firms in 44 four-digit SIC

manufacturing industries (2000-3999).

C) Sample Formation
       The regressions presented in Section IV control for firm fixed effects by first-differencing

the firm-level variables (year and industry dummies are also included in these regressions). In

addition we exclude outliers identified as follows. We remove extreme data points and then

calculate the deviation of each observation from the multivariate mean of all ratio-form variables

(i.e., all but production levels and total assets). We drop observations that are more than two

standard deviations away from the multivariate mean, causing the sample to drop from 8,333 to

7,418 firm-years. This technique improves on univariate winsorizing by incorporating the

multivariate dimension of the data in identifying outliers. First differencing the data causes firms

appearing only once to drop from the sample and lowers the sample count to 6,676 firm-years.

More observations are lost because the regressions include two lags of the regressors as

instruments. The final sample forms an unbalanced panel of 4,584 firm-years comprising 1,051

firms in 44 manufacturing industries over the period 1977-1990.

D) Proxies and Variable Construction

       The measure of financial leverage we use is total debt divided by total assets. We use

book values because the QFR covers both private and public firms and therefore does not contain

market values of securities. For robustness we also merged in COMPUSTAT data into our

sample and estimated our equations using the market value of equity to calculate leverage ratios.

We divided total debt by the market value of equity plus the book value of debt and preferred

stock. Given the LRD data contains public and private firms and given difficulties of matching

                                                                                                 12

the database to COMPUSTAT (matches were done using firm name) our sample of firms was

reduced to 341 (1614 firm years) from 1064 firms (4853 firm years). The results are still similar

in both sign and significance.6 These results reconfirm the findings in previous studies that have

also shown similar results using book and market leverage ratios in cross-sectional studies.7

           In addition to finding similar results for our basic regression, using book values may be

justified by the recent survey by Graham and Harvey (2001) as they report book values is what

managers focus on when setting capital structure. In addition Barclay, Morellec and Smith

(2001) show how book leverage ratios may be the theoretically appropriate measure of leverage

to use in regressions of leverage ratios on firm-level variables given the problems with market

values in the denominator being correlated with firm-level variables.                    We thus present just the

results with the larger sample as private firms are crucial to our later tests that examine entry and

plant sales by smaller nonpublic “fringe” firms as modeled by Williams (1995).

           For capital-intensity (K/L) we use a firm’s fixed-capital stocks (buildings and machinery)

divided by total labor costs (wages plus salaries and benefits). To measure risk, we divide the

standard deviation of operating cash flow by mean sales using up to twenty quarterly

observations. For firms that appear fewer than five years in our sample, this measure is based on

as few as eight quarterly observations. Profitability is operating cash flow divided by sales.

Diversification is one minus the Herfindahl of output across the firm’s four-digit SIC industries.

This measure equals zero for single-industry firms and tends toward one for multi-industry firms.

6
    A table containing these results is available from the authors and also is available on our homepage. In the
    subsample of firms matched to COMPUSTAT we also include a firm’s R&D to sales ratio as a measure of growth
    options. This measure was negative as in the existing literature but it was insignificant in our sample of
    competitive industries.
7
    Rajan and Zingales (1995) consider both book leverage and market leverage measures of capital structure. They
    find that for the U.S. both measures give similar results in their cross-sectional analysis (Table 7). In addition,
    Titman and Wessels (1988) also report in a footnote that their results were similar when using debt to book value
    of assets. They report results using the market value of equity.

                                                                                                                    13

We also control for some of the significant explanatory variables of financial structure

developed in MacKay’s (1998) study of real flexibility. These include volume flexibility, which

is the elasticity of operating cash flow to production level (with a low elasticity indicating a

linear, flexible production technology), and investment flexibility, which is the difference

between the market and shadow rents of fixed-capital stocks (with a small difference indicating a

flexible investment technology). Whereas MacKay considers separate components, here we

combine buildings, machinery, and workforce flexibility into an overall measure of investment

flexibility.

        Finally, we propose two measures of a firm’s position within its industry. First, we use

the absolute value of the difference between the firm’s capital-labor ratio and the mean capital-

labor ratio for its industry-year as a proxy for Maksimovic and Zechner’s (1991) natural hedge.

We refer to this measure as the “natural hedge” in our tables and subsequent analysis. To

facilitate comparison across industries, we normalize this measure such that a value of one

indicates that a firm operates the mean technology for its industry and year, and a value of zero

indicates that a firm operates the technology most unlike the rest of its industry and year.

Maksimovic and Zechner show how firms that adopt the mainstream technology share the cost

structure and fortunes of the bulk of its industry, providing such firms with a natural hedge to

industry shocks. Williams (1995) also classifies firms within an industry on the basis of their

technology, namely, capital-intensive “core” firms and labor-intensive “fringe”. We base our

natural hedge measure on the capital-labor ratio to reflect the central features of both models.

        Another implication of models by Maksimovic and Zechner (1991), Williams (1995), and

Fries et al. (1997) is that even in competitive industries, decisions made by individual firms are

conditioned on the decisions made by the rest of the industry. For instance, Maksimovic and

Zechner show how firms within an industry adjust their debt levels differently in response to

                                                                                                   14

industry-wide shocks, such as a change in the corporate tax rate, depending on their position in

the industry. Because our natural hedge is built around the capital-labor ratio, it may fail to

reflect other dimensions of a firm’s position in its industry. To address this issue, our regressions

control for the mean change in the dependent variable (leverage, capital-intensity, and risk)

inside and outside a firm’s industry-year quintile. Intra-quintile change is the mean change in the

dependent variable for the firm’s industry-year quintile. Extra-quintile change is the mean

change outside the firm’s industry-year quintile. We construct quintiles using the level of the

dependent variables in the preceding year. To avoid spurious correlation, the mean change for

each firm’s own quintile excludes that firm.

       Our instrumental-variable regressions include first and second lags of the regressors and

contemporaneous values of industry-level changes in demand, profitability, volatility, and

downstream demand as instruments. This number of lags reflects a tradeoff between reducing

endogeneity bias, losing observations and introducing large-firm and survivor biases. Our proxy

for industry demand is the total value of shipments for the industry. Because this proxy is partly

endogenous, we use downstream industry shipments to obtain an exogenous measure of industry

demand.

IV. Results

A) Industry and Firm Variation in Capital Structure, Risk and Technology Choice
       Since we are interested in intra-industry variation, we first examine how much variation

arises between and within industries. Table I analyzes the role of industry and firm effects in

explaining financial leverage, capital-intensity, risk, profitability, size, and natural hedge. We

regress each variable on industry dummies to identify the relative importance of two-, three-, and

four-digit SIC industry effects. We also include firm dummies to isolate within-firm variation.

                                        Insert Table I here

                                                                                                  15

Panel A shows that firm effects are the most significant source of variation for financial

leverage, risk, and size. In particular, firm fixed effects account for 64% of the variation in

leverage. Industry effects account for only 8% of the variation in financial leverage, most of

which is explained by two- and three-digit SIC industry effects.8 In other words, most of the

variation in financial structure arises within industries rather than between industries. 9 Tables II

through IV explore this intra-industry variation, and the regressions presented in tables V and

VIII seek to explain the remaining 28% variation in capital structure.

           In contrast to the results for financial leverage, industry effects explain most of the

variation in the capital-labor ratio (47%); mainly at the two-digit SIC level (35%). The remaining

variation is split 34% from firm fixed effects and 20% unexplained variation. These results

suggest that capital-intensity is industry specific, with some cross-sectional firm variation but

little change at the firm-level over time. These findings are consistent with capital-intensity being

fixed once technology is chosen.

           The sources of variation for risk are similar to those observed for financial leverage, with

a stronger industry component and less variation across firms. Perhaps surprisingly, profitability

has practically no industry component, little firm-specificity, and 88% of variation is

unexplained variation. This lack of persistence in profitability suggests that shocks are highly

idiosyncratic and year-specific. Variation in firm size is 28% industry-related but mostly firm

specific (69%) and very persistent over time (only 3% unexplained variation). Finally, natural

hedge variation is 10% industry effects, 36% firm effects, and 53% unexplained variation.

8
    In unreported results, we also do an analysis of variance on time-series averages collapsing all the data into firm-
     specific averages for the entire panel. Collapsing the data does not affect our results. Industry effects at the two
     and three-digit level explain only 9.4% of cross-sectional variation in capital structure, with the four-digit level
     explaining less than one percent of additional variation.
9
    Using the full panel, year effects, while significant, explain less than one percent of the variation in capital
     structure.

                                                                                                                      16

Williams (1995) and Fries et al. (1997) extend the static industry equilibrium modeled in

Maksimovic and Zechner (1991) to a dynamic setting where firms can enter, stay or leave their

industry. Hence, to examine the prevalence of entry and exit and assess it impact on industry

equilibrium, many of our tables divide the full sample into five firm categories: entrants, exiters,

buyers, sellers, and incumbents.

        We distinguish entrants from buyers, and exiters from sellers because it seems reasonable

to expect that by creating or destroying productive capacity, true entry and exit will have a

bigger and perhaps different impact on industry equilibrium than entry and exit through

acquisition or sale of existing plants, which merely reshuffles the ownership of productive

capacity. Thus, panels B to F of Table I analyze industry and firm effects for incumbents,

entrants, exiters, buyers, and sellers.

        Entrants are firms that enter an industry by constructing new plants and running them for

at least two consecutive years in the second half of our empirical period (1984-1990). Exiters are

firms that leave an industry by retiring old plants (not selling them to other firms) after running

them for at least two consecutive years in the first half of our empirical period (1977-1983).

Buyers are firms that enter an industry by acquiring existing plants and running them for at least

two consecutive years in the second half of our empirical period (1984-1990). Sellers are firms

that leave an industry by selling plants to other firms after running them for at least two

consecutive years in the first half of our empirical period (1977-1983). Incumbents are firms that

operate in an industry by owning plants for at least two consecutive years in each half of our

empirical period (1977-1983 and 1984-1990).

        Industry is a greater source of variation for entrants and exiters than in the entire sample

for all of the variables. For example, industry factors explain 45% (38%) of variation in leverage

for entrants (exiters), 46% (42%) for buyers (sellers), versus only 8% (10%) for the entire sample

                                                                                                 17

(incumbents). This suggests that firms enter and leave with “standard” industry-determined

leverage ratios, then move away from the industry norm.10

           As in the entire sample, capital-labor ratios display high industry components in all

subsamples. Unexplained variation (the error term) is much higher for entrants (25%) and exiters

(38%) than for buyers (11%) and sellers (11%), or for the full sample (20%) and the incumbents

(19%). Since the difference is not attributable to greater firm effects (firm effects are lower for

buyers and sellers than entrants and exiters), this concurs with the Williams (1995) equilibrium

outcome: Entrants and exiters are fringe players in terms of technology choice and their initial

and terminal values of capital-intensity. Buyers and sellers, on the other hand, buy and sell plants

with closer to the industry-median technology.

           The idiosyncratic (firm-specific) component of risk for entrants (20%) and buyers (29%)

is far below the levels observed for exiters (45%) and sellers (56%), or even the full sample

(54%) and incumbents (50%). This result, and similar but weaker patterns for profitability,

suggests that new firms in an industry (entrants and buyers) are more sensitive to industry shocks

than incumbents. Firms whose risk or profits continue to display greater sensitivity to industry

and idiosyncratic shocks than incumbents, eventually leave the industry by retiring capacity or

selling their plants.

           These analysis of variance results show that, with the exception of capital-intensity,

industries are less important than firm effects in understanding the variation in firm financial

structure. We find that industry effects are more important for size and especially capital-labor

ratios. Industry effects are more substantial for entrants, buyers, exiters, and sellers. However,

10
     Another possible explanation is that these results are driven by the lower number of firms relative to the number
     of industries represented in these subsamples, causing the industry effects to stand out. For instance, the entire
     sample has 1205 degrees of freedom for firms and 78 (16+34+28) degrees of freedom for industries, a proportion
     of 18:1. This proportion is under 2:1 for panels C to F.

                                                                                                                    18

this may have more to do with the fewer degrees of freedom in those subpanels than a greater

role of industry.

B) Summary Statistics
Table II presents summary statistics for our key variables (financial leverage, capital-intensity,

risk, profitability, size, and natural hedge). As before, we present results for the entire sample

(panel A), entrants and exiters versus incumbents (panels B & D), and buyers and sellers versus

incumbents (panels C & D). Panel A shows that our key variables vary widely across the sample.

Since Table I already examined the role of industry and firm effects in explaining this variation,

we turn immediately to comparison of entrants and exiters, buyers and sellers, and incumbents.

We include industry-year statistics in all these panels to uncover within-industry patterns.

                                       Insert Table II here

       The central column of panel B shows that incumbent firms are larger than entrants and

exiters. These results are consistent with Williams (1995) who predicts industries are formed of a

core of large, profitable, capital-intensive incumbents, flanked by a fringe of small, unprofitable,

labor-intensive, entrants and exiters. Incumbents’ capital-labor ratio is higher than exiting firms,

at least in the pre-exit period. In addition, panel B shows that incumbents carry less debt than

firms that enter and exit, a finding that does not agree with Williams who predicts that core firms

use more debt. Entrants are more highly leveraged than either incumbents or exiters. This

striking difference conforms to Maksimovic and Zechner’s (1991) prediction that firms that

depart from the industry median technology choose high debt ratios. Indeed, entrants are more

capital-intensive, riskier, more profitable (in the year of entry only), and with less similarity to

the industry median technology (less natural hedging) than incumbents. Another explanation is

that entrants adopt the newest, capital-intensive technology that requires large financial infusions

but offers good collateral value and high debt capacity.

                                                                                                 19

Panel B also reports means for post entry and pre exit. Note that the means for entrants

and exiters only reflect the very first or very last year the firm is in the sample. The post entry

column shows means for entrants in the years (maximum six) following the firm’s first year in

the sample. The pre exit column shows means for exiters in the years (maximum six) proceeding

the firm’s last year in the sample. We present these columns to detect differences in years

following entry or leading up to exit, and to highlight the initial and ending characteristics of

entrants and exiters.

           Although entrants appear to change after first entry, because of the small sample size,

none of the observed differences between the year they enter and afterward are statistically

significant. Nevertheless, some differences are worth noting. Financial leverage, capital-

intensity, risk, and size, all increase following entry. Profitability and natural hedge decrease.

Collectively, these changes show entrants shifting to riskier strategies post entry.

           A somewhat stronger story emerges for exiters. For instance, risk increases noticeably

though not significantly in the last year, perhaps reflecting an end-game strategy, or simply the

fact that diversification has fallen (not reported). Profitability also declines in the last year,

indicative of economic distress. Since the financial leverage of exiters remains close to the mean

for incumbents before and during the year of exit, it seems safe to infer that these firms are not

experiencing financial distress. The capital-labor ratio falls pre-exit, perhaps because exiters are

cutting back on capital costs in their final year. Size also decreases in the last year, as exiters sell

some assets prior to leaving the industry. 11

           Panel C reports means for the first year (buy) and subsequent years (post buy) buyers

enter an industry, and the years preceding (pre sell) and the last year (sell) sellers leave their

industries. Buyers incur significantly more risk and earn lower returns in the years following

11
     In an unreported analysis, we find that the number of plants owned by exiters falls substantially prior to exit.

                                                                                                                        20

entry. Similar to the pattern observed in Panel B, Panel C shows that buyers are significantly

more financially leveraged than incumbents or sellers. In fact, sellers lower their indebtedness

significantly relative to incumbents and before leaving the industry.

C) Intra-Industry Dispersion Patterns
       Table III continues our analysis of intra-industry variation by presenting quintile means

for our key variables (financial leverage, capital-intensity, risk, profitability, size, and natural

hedge). We construct quintiles based on each of these variables and report means for each

quintile. The quintiles are formed for each of the 44 four-digit SIC industries and for each of the

17 years spanned by the sample (748 industry-year quintiles). Table III reports industry-year

adjusted means for the first, third, and fifth quintile. We make the industry-year adjustments as

we wish to examine dispersion patterns within industries by removing industry-year effects. The

table contains six panels because we construct quintiles based on each of our six key variables.

       We present these quintiles to detect nonlinear patterns across firms but within industries

to examine the predictions advanced by Maksimovic and Zechner (1991) and Williams (1995)

regarding the equilibrium structure of industries along the dimensions of debt, technology and

risk. Maksimovic and Zechner, for instance, predict that firms with atypical technologies carry

more debt and more risk. Williams predicts that the equilibrium structure of industries is

characterized by a core of large, profitable, capital-intensive, financially leveraged firms flanked

by a fringe of small, risky, less profitable labor-intensive firms. A standard correlation analysis

between the variables would not pick up these nonlinear patterns.

                                      Insert Table III here

       The first panel of Table III shows how financial leverage and other variables vary when

each industry is divided into quintiles based on financial leverage itself. It is apparent that

financial leverage within each industry exhibits substantial variation. Quintile one has an

                                                                                                   21

industry-year adjusted mean of –0.195 versus 0.248 for quintile five. Although the range for the

capital-labor ratios between quintiles one and five is narrow, capital-intensity does increase

significantly in financial leverage, consistent with capital-intensity increasing debt capacity. Risk

is lowest at the center of the industry (quintile 3), and peaks in quintiles one and five.

Profitability, size, and natural hedge are lowest in quintiles one and five. Together, these results

support a Williams-type equilibrium, with fringe firms in terms of risk, profitability and size

clustered in the tails of the leverage distribution.

        Our finding that natural hedge is significantly lower in the fifth leverage quintile is

consistent with Maksimovic and Zechner’s (1991) prediction that firms which deviate from their

industry’s mainstream technology carry more debt. However, this relation does not hold

uniformly across the industry since natural hedge is lower in the first leverage quintile than in the

third leverage quintile (though the difference is not significant).

        Turning to the capital-labor quintiles shown in the second panel of Table III, we find that

size is not linearly related to capital-intensity, contrary to what a partial-equilibrium model might

predict. Rather, small firms are either labor-intensive (quintile one) or capital-intensive (quintile

five), a finding that supports the idea that small firms operate at the fringe of an industry,

choosing atypical technologies. We also find that financial leverage and risk peak in the fifth

quintile whereas profitability declines. This suggests an economic distress effect where less

profitable firms take on more debt and risk as their profitability falls. This is even more apparent

in the profitability quintiles panel, where financial leverage is 0.044 in for the least profitable

firms (quintile one), and –0.036 for the most profitable firms (quintile five). Quintile one firms

are significantly riskier, smaller, than median firms (quintile three), again supporting a core-

fringe industry structure as in Williams (1995).

                                                                                                  22

The risk quintiles panel brings out two other relations. First, we find a monotonic positive

relation between capital-intensity, which is often viewed a measure of ex-ante risk, and cash flow

volatility (risk), which measures realized ex post risk. Second, we find partial support for

Maksimovic and Zechner’s (1991) prediction that firms which deviate from their industry’s

mainstream technology display greater and cash flow volatility. However, this relation does not

hold uniformly across the industry since natural hedge is lower in the first risk quintile than in

the third leverage quintile (though the difference is not significant).

       The profitability quintiles panel exhibits a U-shaped pattern for risk. We find that firms

of median profitability exhibit lower risk, suggesting that we are observing ex-post outcomes.

Firms choose higher risk and, ex post, we observe both low and high profitability. Corroborating

this interpretation is the fact that median firms use significantly less operating leverage (lower

capital-labor ratios) than both quintile one and quintile five firms. We also detect an economic

distress effect in this panel as low profitability coincides with high financial leverage.

       We also analyzed the industries in Table III splitting the industries into those with high

and low dispersion in capital structure. We contrast low and high dispersion industries because

in some industries technological factors may prevent firms from having much variation in their

capital stock and thus we may not observe much variation in capital structure and the other

variables. We do not report this analysis, as the results were similar to those in Table III. These

results are available from the second author’s website. Even in low dispersion industries, the

range in financial leverage is quite wide: -0.142 in quintile one and 0.159 in quintile five (for

high dispersion industries we find –0.245 in quintile one and 0.345 in quintile five). We did find

that patterns observed in Table III are accentuated in high dispersion industries.

                                                                                                23

D) The Relationship Between Capital Structure, Risk and Technology Choice
        The intra-industry patterns presented so far in our univariate statistics lend support to the

industry equilibrium models. This section provides a multivariate regression analysis to

determine whether these patterns and other predictions obtain in an estimation framework that

allows for endogeneity in the regressors and simultaneity between three decision variables:

financial leverage, capital-intensity, and risk.

        Table IV presents ordinary least square (OLS) and three stage least squares (3SLS)

results. In contrast to OLS, 3SLS controls for simultaneity of the dependent variables by

incorporating the correlation of residuals across the three equations. This improves the efficiency

and consistency of the estimates. Moreover, as an instrumental-variables (IV) estimation method,

3SLS mitigates simultaneity bias caused by including endogenous explanatory variables in the

regression model. We instrument the regressors by their first and second lags and proxies for

industry-level demand, profitability and volatility (as explained in section III).

        Given the panel nature of our sample, we control for firm fixed-effects by first-

differencing the continuous variables appearing in the regression model. This reduces omitted-

variable bias and dampens idiosyncratic measurement and estimation error by removing firm-

specific factors from the data. Because we are interested in intra-industry variation, we also

control for four-digit SIC industry and year fixed effects

                                        Insert Table IV here

        Inspection of the 3SLS results for leverage shows that leverage is positively related to the

capital labor ratio, risk and profitability. There are also some striking differences between OLS

and 3SLS results. In particular, our 3SLS results show a positive significant relation between

financial leverage and risk, whereas our OLS results show no significant relation. Past empirical

work has found the relation between leverage and risk to be positive (Kim and Sorensen (1986)),

                                                                                                  24

negative (Bradley, Jarrell, and Kim (1984)), and insignificant (Titman and Wessels (1988).

Besides lending credence to Maksimovic and Zechner’s (1991) prediction of a positive relation

between financial leverage and risk, our finding also suggests that the split in results in the

literature might be due to inconsistent treatment of simultaneous decision variables and

endogenous explanatory variables.

           The finding of a positive relation between the change in financial leverage and the change

in profitability within an industry is also of particular note. This relation is also different across

OLS and 3SLS.12 This relation is significant but negative in the OLS regressions, which is

consistent with prior studies that only report OLS results and fail to address the issues of

simultaneity and endogeneity emphasized here. Also significant in the 3SLS results is the

positive relation between changes in the capital labor ratio and investment flexibility and a

positive relation between changes in risk and volume flexibility. These results coincide with a

negative relation between changes in investment flexibility and capital structure, suggesting that

lenders take into account possible risk shifting in their setting of capital structure.

           Another important result we highlight is the significant negative relation between

changes in financial leverage and a firm’s technology choice relative to its industry – a firm’s

natural hedge. This variable ranges from zero to one and equals one when the firm’s technology

choice is like its industry median. The natural hedge variable is used to test Maksimovic and

Zechner’s (1991) prediction that firms that deviate from their industry’s mainstream technology

also use higher financial leverage. We include the square of this variable as well given the

previous univariate results that showed a non-linear relationship between financial leverage and a

firm’s departure from the industry median technology. Table IV shows that the natural hedge

12
     This result is also consistent with Graham (1996a,b) as Graham finds that a dummy variable for taxable income is
     a good proxy for a firm’s marginal tax rate and that firm is more likely to issue debt when its marginal tax rate is
     high.

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